git《Investigating Loss Functions for Extreme Super-Resolution》(CVPR 2020) GitHub:

Overview

Investigating Loss Functions for Extreme Super-Resolution

NTIRE 2020 Perceptual Extreme Super-Resolution Submission.

Our method ranked first and second in PI and LPIPS measures respectively.

[Paper]

Dependency

  • Python 3.6
  • PyTorch 1.2
  • numpy
  • pillow
  • tqdm

Test

  1. Clone this repo.
git clone https://github.com/kingsj0405/ciplab-NTIRE-2020
  1. Download pre-trained model and place it to ./model.pth.
  1. Place low-resolution input images to ./input.

  2. Run.

python test.py

If your GPU memory lacks, please try with option -n 3 or a larger number.

  1. Check your results in ./output.

Train

  1. Clone this repo.
git clone https://github.com/kingsj0405/ciplab-NTIRE-2020
  1. Prepare training png images into ./train.

  2. Prepare validation png images into ./val.

  3. Open train.py and modify user parameters in L22.

  4. Run.

python train.py

If your GPU memory lacks, please try with lower batch size or patch size.

BibTeX

@InProceedings{jo2020investigating,
   author = {Jo, Younghyun and Yang, Sejong and Joo Kim, Seon},
   title = {Investigating Loss Functions for Extreme Super-Resolution},
   booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
   month = {June},
   year = {2020}
}

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